Search Results (519)

Monitoring muscle fatigue is essential for ensuring safety and maximizing the effectiveness of resistance training. Conventional methods such as electromyography (EMG), inertial measurement units (IMU), and ratings of perceived exertion (RPE) involve complex procedures and have limited applicability, particularly in unsupervised or robotic exercise environments. This study proposes a machine learning-based approach to directly predict RPE from force–time data collected during repeated isokinetic bench press sets. Thirty-two male participants (64 limb datasets) performed seven sets at a standardized 7RM load, with load cell data and RPE scores recorded. Biomechanical features representing magnitude, variability, energy, and temporal dynamics were extracted, along with engineered features reflecting relative changes and inter-set variations. The findings indicate that RPE is more closely related to relative fatigue progression than to absolute biomechanical output. Incorporating engineered features substantially improved predictive performance, with the Random Forest model achieving the highest accuracy and more than 93% of predictions falling within ±1 RPE unit of the reported values. The proposed approach can be seamlessly integrated into intelligent resistance machines, enabling automated load adjustment and providing substantial potential for applications in both athletic training and rehabilitation contexts. Full article

Reclaimed coastal areas are highly susceptible to uneven subsidence caused by the consolidation of soft marine deposits, which can induce differential settlement, structural deterioration, and systemic risks to urban infrastructure. Further, engineering activities, such as construction and loadings, exacerbate subsidence, impacting infrastructure stability. Therefore, monitoring the integrity and vulnerability of linear urban infrastructure after construction on reclaimed land is critical for understanding settlement dynamics, ensuring safe and reliable operation and minimizing cascading hazards. Subsequently, in the present study, to monitor deformation of the linear infrastructure constructed over decades-old reclaimed land in Mokpo city, South Korea (where 70% of urban and port infrastructure is built on reclaimed land), we analyzed 79 Sentinel-1A SLC ascending-orbit datasets (2017–2023) using the Persistent Scatterer Interferometry (PSInSAR) technique to quantify vertical land motion (VLM). Results reveal settlement rates ranging from −12.36 to 4.44 mm/year, with an average of −1.50 mm/year across 1869 persistent scatterers located along major roads and railways. To interpret the underlying causes of this deformation, Casagrande plasticity analysis of subsurface materials revealed that deep marine clays beneath the reclaimed zones have low permeability and high compressibility, leading to slow pore-pressure dissipation and prolonged consolidation under sustained loading. This geotechnical behavior accounts for the persistent and spatially variable subsidence observed through PSInSAR. Spatial pattern analysis using Anselin Local Moran’s I further identified statistically significant clusters and outliers of VLM, delineating critical infrastructure segments where concentrated settlement poses heightened risks to transportation stability. A hyperbolic settlement model was also applied to anticipate nonlinear consolidation trends at vulnerable sites, predicting persistent subsidence through 2030. Proxy-based validation, integrating long-term groundwater variations, lithostratigraphy, effective shear-wave velocity (V_s³⁰), and geomorphological conditions, exhibited the reliability of the InSAR-derived deformation fields. The findings highlight that Mokpo’s decades-old reclamation fills remain geotechnically unstable, highlighting the urgent need for proactive monitoring, targeted soil improvement, structural reinforcement, and integrated InSAR-GNSS monitoring frameworks to ensure the structural integrity of road and railway infrastructure and to support sustainable urban development in reclaimed coastal cities worldwide. Full article

Accurate heavy-duty vehicle load estimation is crucial for transportation and environmental regulation, yet current methods lack precision in data accuracy and practicality for field implementation. We propose a Self-Supervised Reconstruction Heterogeneous Graph Convolutional Network (SSR-HGCN) for load estimation using On-Board Diagnostics (OBD) data. The method integrates physics-constrained heterogeneous graph construction based on vehicle speed, acceleration, and engine parameters, leveraging graph neural networks’ information propagation mechanisms and self-supervised learning’s adaptability to low-quality data. The method comprises three modules: (1) a physics-constrained heterogeneous graph structure that, guided by the symmetry (invariance) of physical laws, introduces a structural asymmetry by treating kinematic and dynamic features as distinct node types to enhance model interpretability; (2) a self-supervised reconstruction module that learns robust representations from noisy OBD streams without extensive labeling, improving adaptability to data quality variations; and (3) a multi-layer feature extraction architecture combining graph convolutional networks (GCNs) and graph attention networks (GATs) for hierarchical feature aggregation. On a test set of 800 heavy-duty vehicle trips, SSR-HGCN demonstrated superior performance over key baseline models. Compared with the classical time-series model LSTM, it achieved average improvements of 20.76% in RMSE and 41.23% in MAPE. It also outperformed the standard graph model GraphSAGE, reducing RMSE by 21.98% and MAPE by 7.15%, ultimately achieving < 15% error for over 90% of test samples. This method provides an effective technical solution for heavy-duty vehicle load monitoring, with immediate applications in fleet supervision, overloading detection, and regulatory enforcement for environmental compliance. Full article

This study experimentally compares classical PID and three adaptive control strategies (including a novel adaptive control strategy developed by the authors) for stabilizing the crankshaft speed of a diesel engine (ADCR Euro 4). The tests were performed on a dynamometer with alternator-induced step loads. All tests were performed at a constant engine crankshaft speed using National Instruments instrumentation and custom LabVIEW-based software for real-time monitoring. Metrics included response time (RT), overshoot (OV), and steady-state error (SSE), each based on ten repetitions with reported standard deviations. Results show that the competitive adaptive algorithm reduced RT by ~20%, OV by ~15%, and SSE by ~10% compared to PID. These results confirm that adaptive control, especially the competitive strategy, provides high precision and fast disturbance rejection, bridging the gap between simulation-based studies and industrial diesel engine applications. These results highlight the potential of adaptive control in applications such as air–fuel ratio control, turbocharger pressure control, knock detection, and fuel optimization. Full article

With the increasing age and traffic load of highway networks in China, preventive maintenance has become a critical strategy for extending pavement service life and improving infrastructure sustainability. However, the lack of standardized post-evaluation systems has hindered the scientific assessment of maintenance effectiveness. This study proposes a systematic post-evaluation framework for highway preventive maintenance projects based on the Hazard Analysis and Critical Control Points (HACCP)-Inspired methodology (Applying Principles of Hazard Analysis and CCP Identification). Adopting a full life-cycle perspective, the framework identifies critical control points (CCPs) across pre-, mid-, and post-implementation phases, targeting six key dimensions: ecological and environmental hazards, resource utilization hazard, engineering safety risks, engineering quality risks, socioeconomic benefit hazards, and social living environment hazards. A multi-level evaluation indicator system is constructed using hierarchical clustering and weighted through the Analytic Hierarchy Process (AHP). The framework is applied to a preventive maintenance project on the Jinghuan Expressway in Tianjin, China, demonstrating strong practical applicability. The final evaluation score of 84.1 out of 100 confirms the technical adequacy of the project while revealing areas for improvement in clean energy adoption and substructure monitoring. This framework provides a robust basis for standardizing post-evaluation practices and promoting sustainable highway maintenance management. Full article

In line with EU decarbonization targets for the heavy-duty transport sector, this study proposes an analytical methodology to assess the impact of diesel performance additives on fuel consumption in Euro 6 heavy-duty vehicles, the prevailing standard in the circulating European road tractor fleet. A fleet of five N3-category road tractors equipped with tanker semi-trailers was monitored over two phases. During the first 10-month baseline phase, the vehicles operated with standard EN 590 diesel (containing 6–7% FAME); in the second phase, they used a commercially available premium diesel containing performance-enhancing additives. Fuel consumption and route data were collected using a GPS-based system interfaced with the engine control unit via the OBD port and integrated with the fleet tracking platform. After applying data filtering to exclude low-quality or non-representative trips, a 1% reduction in fuel consumption was observed with the use of fuel with additives. Route-level analysis revealed higher savings (up to 5.1%) in high-load operating conditions, while most trips showed improvements between −1.6% and −3.4%. Temporal analysis confirmed the general trend across varying vehicle usage patterns. Aggregated fleet-level data proved to be the most robust approach to mitigate statistical variability. To evaluate the potential impact at scale, a European scenario was developed: a 1% reduction in fuel consumption across the 6.75 million heavy-duty vehicles in the EU could yield annual savings of 2 billion liters of diesel and avoid approximately 6 million tons of CO₂ emissions. Even partial adoption could lead to meaningful environmental benefits. Alongside emissions reductions, fuel additives also offer economic value by lowering operating costs, improving engine efficiency, and reducing maintenance needs. Full article

The extraction of geothermal energy is of great significance for sustainable energy development. The destruction of hard rock masses during geothermal well exploitation generates seismic waves that can compromise wellbore stability and operational sustainability. Seismic waves are known to be affected by rock structures like cracks and interfaces. However, a quantitative understanding of these effects on wave parameters is still lacking. This study addresses this gap by experimentally investigating the effect of crack geometry (angle and width) and rock interfaces on seismic wave propagation. Using a synchronous system for rock loading and seismic wave acquisition, we analyzed wave propagation through carbonate rock samples with pre-defined cracks and interfaces under unconfined, dry laboratory conditions. Key wave parameters (amplitude, frequency, and energy) were extracted using the fast Fourier transform (FFT) and the Hilbert–Huang transform (HHT). Our primary findings show the following: (1) Increasing the crack angle from 35° to 75° and the width from 1 mm to 3 mm leads to significant attenuation, reducing peak amplitude by up to 94.0% and energy by over 99.8%. (2) A tightly pressed rock interface also causes severe attenuation (94.2% in amplitude and 99.9% in energy) but can increase the main frequency by up to 8.5%, a phenomenon attributed to a “boundary effect”. (3) Seismic wave parameters exhibit significant spatial variations depending on the propagation path relative to the source and rock structures. This study provides a fundamental, quantitative baseline for how rock structures govern seismic wave attenuation and parameter shifts, which is crucial to improving microseismic monitoring and wellbore integrity assessment in geothermal engineering. Full article

Extracellular Vesicle-associated microRNAs (EV-miRNAs) are emerging as pivotal regulators of corneal health and disease, holding exceptional promise for transforming both diagnostics and therapeutics. These vesicles carry distinct miRNA signatures in biofluids such as tears, offering a powerful, non-invasive approach for early detection, risk stratification, and dynamic monitoring of corneal disorders. In addition, EV-miRNAs act as key mediators of critical biological processes, including inflammation, fibrosis, and tissue repair. Consequently, they represent attractive therapeutic targets; for example, engineered EVs loaded with miRNA mimics or inhibitors can precisely modulate these pathways to promote regeneration and suppress disease progression. Yet, despite this considerable promise, the translation of EV-miRNA research into clinical practice remains constrained by several challenges. Topmost among these are the lack of standardized EV isolation methods, variability in miRNA quantification, and the pressing need for regulatory frameworks tailored to the complexity of these biological therapeutics. Addressing these barriers is essential to ensure reproducibility, scalability, and safety in clinical applications. Accordingly, this review synthesizes current knowledge on EV-miRNA profiles in corneal diseases, critically evaluates their diagnostic and therapeutic potential, and highlights strategies to overcome existing technical and regulatory limitations. Ultimately, the successful integration of EV-miRNA-based approaches into personalized medicine frameworks could revolutionize the management of corneal diseases and substantially improve patient outcomes. Full article

Residual stresses play a crucial role in the maintenance and longevity of engineering structures. However, continuous monitoring of these stresses remains a challenge due to cost, implementation complexity, and reliability concerns. The present contribution proposes a novel method for continuous long-term residual stress monitoring by tracking the effect of residual stress changes on the propagation velocity of surface acoustic waves (SAWs) due to the acoustoelastic effect via a fixed setup of piezoelectric patch transducers (PETs). The applicability of patch transducers to stress measurement using SAW was experimentally validated using tensile and bending tests on 25CrMo4 (1.7218) steel specimens. The tensile tests exhibited a consistent decrease in wave velocity with increasing stress, enabling straightforward determination of the acoustoelastic coefficient (AEC). The bending tests confirmed the method’s applicability, highlighting the need for multiple excitation frequencies to improve reliability and detect inconsistencies. Finally, it is briefly outlined how to separate residual and load stresses during long-term measurements. The results demonstrate that this approach provides a cost-effective solution for continuous monitoring of residual stresses in metallic materials, offering potential applications in structural health monitoring and predictive maintenance. Full article

Commercial buildings and shopping malls face rising electricity costs and increasing pressure to adopt sustainable practices. This paper presents the first long-term, multi-site empirical validation of Automatic Voltage Regulator (AVR) deployment in Thai retail facilities, providing robust evidence for tropical, motor-heavy load contexts. The study evaluates the engineering, economic, and environmental performance of an AVR with an autotransformer core under real operating conditions. High-resolution measurements were collected before and after AVR installation, using Class 0.2s analyzers and a Building Energy Management System (BEMS) across multiple branches during a four-month monitoring campaign (February–May). Results indicate that a modest voltage reduction of 8.06% yielded a 12.02% decrease in active power demand, a 6.22% current reduction, and a 2.26% improvement in power factor. The greatest savings occurred in HVAC (8.19%) and refrigeration loads (8.20%), while lighting loads remained nearly unchanged. Economically, the system delivered ~177 kWh/day savings, equivalent to 262,212 THB/year, with a simple payback of 2.67 years and an ROI of 37.5%. Environmentally, the AVR reduced 36.6 tCO₂/year (±5%), aligning with Thailand’s Energy Efficiency Plan (EEP) 2018–2037 and Carbon Neutrality Roadmap and offering additional potential for T-VER monetization. These findings confirm AVR technology as a scalable, standards-compliant, and high-return retrofit solution for commercial facilities in tropical climates. Full article

The increasing reliance on lithium-ion batteries (LIBs) in electric vehicles (EVs) has intensified the need for structurally resilient and lightweight protective enclosures that can withstand mechanical abuse during crashes. This study addresses the challenge by drawing inspiration from the hierarchical geometry of bighorn sheep horns to design a bio-inspired battery frame with improved crashworthiness. A multilayered structure, replicating both the internal and external features of the horn, was fabricated using Fused Deposition Modeling (FDM) with Acrylonitrile Butadiene Styrene (ABS) and carbon fiber composite (CFC) materials. The experimental evaluation involved tensile and compression testing, Izod impact tests, digital image correlation (DIC), and acoustic emission (AE) monitoring for full-field strain mapping, aiming to assess structural performance under various loading scenarios. Results demonstrate that the bioinspired designs exhibit enhanced energy absorption, mechanical strength, and strain distribution compared to conventional configurations. The improved vibration response and damage tolerance observed in structured samples suggest their potential for application in battery protection systems. This work underscores the feasibility of leveraging natural design principles to engineer robust, lightweight enclosures for advanced energy storage systems, contributing to safer and more reliable EV technologies. Full article

Natural rock materials, containing micro-cracks and pore defects, significantly alter their mechanical behavior. This study investigated fracture interactions of red sandstone containing double close-round holes (diameter: 10 mm; bridge angle: 30°, 45°, 60°, 90°) using acoustic emission (AE) monitoring and the discrete element simulations method (DEM), which was a novel methodology for revealing dynamic failure mechanisms. The uniaxial compression tests showed that hole geometry critically controlled failure modes: specimens with 0° bridge exhibited elastic–brittle failure with intense AE energy releases and large fractures, while 45° arrangements displayed elastic–plastic behaviors with stable AE signal responses until collapse. The quantitative AE analysis revealed that the fracture-type coefficient k had a distinct temporal clustering characteristic, demonstrating the spatiotemporal synchronization of tensile and shear crack initiation and propagation. Furthermore, numerical simulations identified a critical stress redistribution phenomenon, that axial compressive force chains concentrated along the loading axis, forming continuous longitudinal compression zones, while radial tensile dispersion dominated hole peripheries. Crucially, specimens with 45° and 90° bridges induced prominently symmetric tensile fractures (85° to horizontal direction) and shear-dominated failure near junctions. These findings can advance damage prediction in discontinuous geological media and offer direct insights for optimizing excavation sequences and support design in cavern engineering. Full article

Photogrammetry offers a non-contact and efficient alternative for monitoring structural deformation and is particularly suited to large or complex surfaces such as masonry walls. This study proposes a spatio-temporal photogrammetric refinement framework that enhances the accuracy of three-dimensional (3D) deformation and strain analysis by integrating advanced filtering techniques into markerless image-based measurement workflows. A hybrid methodology was developed using natural image features extracted using the Speeded-Up Robust Features algorithm and refined through a three-stage filtering process: median absolute deviation filtering, Gaussian smoothing, and representative point selection. These techniques significantly mitigated the influence of noise and outliers on deformation and strain analysis. Comparative experiments using both manually placed targets and automatically extracted feature points on a full-scale masonry wall under destructive loading demonstrated that the proposed spatio-temporal filtering effectively improves the consistency of displacement and strain fields, achieving results comparable to traditional marker-based methods. Validation against laser rangefinder measurements confirmed sub-millimeter accuracy in displacement estimates. Additionally, strain analysis based on filtered data captured crack evolution patterns and spatial deformation behavior. Therefore, integrating photogrammetric 3D point tracking with spatio-temporal refinement provides a practical, accurate, and scalable approach to monitor structural deformation in civil engineering applications. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article

The automation of concrete constructions through 3D printing (3DP) has been increasingly developed and adopted in civil engineering due to its promising advantages over traditional construction methods. However, widespread implementation is hindered by uncertainties in quality control, homogeneity, and interlayer bonding, as well as the uniqueness of each printed component. Building upon our prior work in developing 3D-printable self-sensing cementitious materials by incorporating graphite powder and carbon microfibers into a cementitious matrix to enhance its piezoresistive properties, this study aims at enabling condition assessment of cementitious 3DP by integrating the self-sensing materials as sensing nodes within conventional components. Three different 3D-printed strip patterns, consisting of one, two, and three strip lines that mimic the pattern used in fabricating foil strain gauges were investigated as conductive electrode designs to impart strain sensing capabilities, and characterized from a series of quasi-static and dynamic tests. Results demonstrate that the three-strip design yielded the highest sensitivity (${\lambda }_{\mathrm{stat}}$ of 669, ${\lambda }_{\mathrm{dyn}}$ of 630), whereas the two-strip design produced the highest signal quality (SNR_stat = 9.5 dB, SNR_dyn = 10.8 dB). These findings confirm the feasibility of integrating 3D-printed self-sensing cementitious materials through hybrid manufacturing, enabling monitoring of print quality, detection of load path changes, and identification of potential defects. Full article